1. Field of Invention
This invention relates to the detection of new events.
2. Description of Related Art
New event detection systems determine whether a story event described in radio or television broadcasts, newspaper articles or other information sources differ sufficiently from previous stories to be categorized as a new event. New event detection systems differ from conventional categorization systems in that the events to be detected occur frequently but have not been seen before. Thus, effective new event detection systems must devise strategies for learning from new input or must determine ways of characterizing the input independent of the particular event.
Yang describes a conventional new event detection system that combines term frequency inverse-document frequency models with different clustering techniques for historical and look ahead data. See Yang et al., “A Study on Retrospective and On-Line Event Detection”, in Proceedings of SIGIR-9, Melbourne, Australia, 1998. Zhang describes document classification by broad topic categories and topic-based new event detection based on topic conditioned stop words and weighting. See Zhang et al., “Novelty and Redundancy Detection in Adaptive Filtering”, in Proceedings of SIGIR-02, Tampere, Finland, 2002, p. 81-88.
Allan describes a conventional system event detection system using incremental term frequency inverse document frequency models, single link measures, topic based models and relevance models. See Allan et al., “On-Line New Event Detection and Tracking”, Proceedings of the SIGIR-98, Melbourne, Australia, 1998, p. 37-45. Franz describes the use of temporal features such as cluster recency to improve new event detection. See www.itl.nist.gov/iaui/894.01/tests/tdt/tdt2001/PaperPres/ibm-pres/tdt2001_nn.ppt, as downloaded Jul. 24, 2004.